Machine learning makes metal 3D printing more efficient

Russian researchers have used machine learning to make metal 3D printing more efficient.

3D printers require fine tuning of positioning and control algorithms using mathematical models to reach optimal performance. This is a lengthy and arduous process and it could take weeks to set printing parameters. Even then, the possibility of printing error is always present.

Going further, scientists are also exploring neural networks to make AI more capable in 3D printing.

A neural network is a framework for deep machine learning inspired by the connectionist architecture of the brain neurons. It is a mode of communication and feedback response which a machine uses to become capable of self-learning. Neural networks are different from AI algorithms, as they do not use task-specific rules.

Using a neural network, a computer can develop image recognition abilities, among others capabilities. For example, it can take a manual input of an image of ‘dog’ and ‘not-dog’. With this data, the machine could develop a criterion of difference between what is a dog and what is not a dog. And in future, use this as an image recognition criteria. Google image CAPTCHA is a neural network which works on this principle.

The SPbPU neural network for 3D printing was developed in MATLAB, a numerical computing software, and a programming language.

Using the new neural network, SPbPU team developed printing modes to manufacture ship mastheads. SPbPU scientists are further testing the developed neural network. So far they have tested the quality of laser melting, the quality of manufactured parts, and the stability of the welding process.

Oleg Panchenko, Head of the Laboratory of Lightweight Materials and Structures SPbPU, elaborated “The next step is to create an online system based on the neural network with automatical input of data sets and output of parameters, thus such system will be learning continuously. We believe that the new system will improve the quality of the parts and increase the speed of parameters development for further manufacturing.”